AI/ML Engineer

About This Role

Allstate's Data & Analytics Technology organization needs a Machine Learning Platform Lead Engineer. You'll be the person who architects, builds, and scales the core platforms that power machine learning solutions across the entire enterprise. This isn't a junior role ; it's for a senior technologist who wants to do hands on engineering while also providing real technical leadership.

You'll work on ML infrastructure, MLOps automation, model deployment systems, and cloud native engineering. The role involves influencing platform strategy and making architectural decisions alongside data science, engineering, security, and product teams. The goal is to enable reliable, scalable, and responsible ML adoption that the whole company can use.

What You Would Be Doing

• Serving as the technical lead for ML platform architecture, guiding system design, scalability, performance, and reliability across all platform components
• Architecting and building core ML platform services including training and compute infrastructure, feature stores, model registries, inference runtimes, and data pipelines
• Driving architectural decisions for distributed systems, cloud native frameworks, and automated MLOps workflows that support enterprise scale machine learning
• Evaluating and integrating emerging ML platform technologies, tools, and best practices to strengthen platform capabilities
• Designing and implementing MLOps pipelines for experiment tracking, data and model versioning, CI/CD for ML, automated retraining, and model governance
• Developing automated workflows that ensure reproducible model training, validation, deployment, and lifecycle management across multiple environments
• Implementing monitoring and observability systems for model performance, data quality, drift detection, and inference reliability
• Building and optimizing cloud based ML infrastructure on Azure, AWS, or GCP using Kubernetes, containerization, and infrastructure as code
• Developing scalable batch and streaming data pipelines using modern data engineering tools and frameworks
• Embedding security, compliance, responsible AI principles, and cost optimization best practices into ML platform architecture and operations
• Collaborating with data scientists to translate modeling needs into scalable, reusable, and self service platform capabilities
• Working closely with security, compliance, and governance teams to ensure safe and compliant deployment of AI/ML solutions
• Partnering with application engineering teams to accelerate adoption of ML services and enable consistent, high quality production deployments
• Providing technical mentorship, setting engineering standards, and contributing to documentation, best practices, and ongoing platform improvements

What You Will Need

• Extensive experience in ML engineering, platform engineering, or large scale distributed systems
• Deep hands on expertise with MLOps tools, ML frameworks, model deployment techniques, and ML lifecycle automation
• Strong proficiency in Python and backend development for machine learning systems
• Experience with cloud platforms and ML services including Azure ML Studio, AWS SageMaker, and Google Vertex AI
• Exposure to cloud storage or data tools such as Azure Fabric or OneLake, AWS S3, and Google Cloud Storage (GCS)
• Experience with cloud native scanning and security tools like Azure Defender, Microsoft Purview, AWS Security Hub, Amazon Inspector, GCP Security Command Center, or equivalent services
• Strong understanding of Kubernetes, Docker, CI/CD, Terraform or Infrastructure as Code, and similar technologies
• Solid knowledge of system design, APIs, data pipelines, and scalable ML infrastructure patterns
• Proven ability to lead technical initiatives and influence cross team engineering decisions
• 6 or more years of related experience is preferred

Supervisory Responsibilities

This job does not have supervisory duties.

Compensation

Base compensation offered for this role is $110,000.00 to $160,000.00 annually. The actual amount is based on job related factors such as skills, experience, and education or training.

How to Apply

Apply through JobXi with your resume and a brief note on your experience with ML platform engineering. We'll review applications and reach out if there's a match.